General Engineering Introduction/Error Analysis
Evil is live spelled backwards. Error is uncertainty with a connotation of something being wrong. But there is nothing wrong. The Uncertainty Analysis is a much better term than Error Analysis. An engineer is uncertain, not in error. Understanding the math of "Error Analysis" helps boost an engineer's confidence. But we are stuck with "US Elementary through High School education" right/wrong tests. We need to become comfortable documenting current uncertainty and failure. Error analysis can help clarify, identify, prove the problem can't be fixed or even fix the problem. This is not random torture teachers put you through.
How close did I get? 
US Elementary through High School education (K-12) computes percent error that is measured according to some formula such as:
This is not error or uncertainty analysis. It is a measure of "How close did I get?" It does teach respect of STEM. It does help reduce the occult and magic. But it doesn't teach:
- repeating, reproducing success/failure analysis
- isolation of error sources
- uncertainty expectations
- analysis dependence upon statistics and calculus
Error Analysis 
There are two reasons for error analysis:
- proving experiment or project replication success
- identifying improvement possibilities
Success in science involves replicating experiments. Success in engineering involves replicating projects. Replication success involves comparing the original documentation with the new experiment or project. This typically involves numbers and a comparison of numbers that looks something like this:
If the known value is within the margin of error, then the experiment is a success. Error analysis is the process of choosing the math technique to use, figuring where the error comes from, and what can be done to reduce it. This can lead to an engineering career in risk analysis or quality control.
Error comes from process such as mixing too little in chemistry. Error comes from tools with limitations. Error comes from parts purchased that are then assembled to display even more error. There are two types of error: systematic and random. Learn how to identify them: Measurement Error
According to the National Institute of Standards and Technology, there are two types of uncertainty analysis methods:
- everything else
When does an engineer use statistics rather than make one measurement? When can error be assumed random? Read Statistics Analysis to figure out how and when to approximate error using statistics or repeated measurement.
Everything Else 
How does the error of each step in a chemistry experiment combine? How do the errors of each component in a circuit combine? What does NIST mean by "everything else" above? Suppose we have a nominal value with a ± error, either from statistics or direct measurement. How do these errors combine? There are classes you may take in this subject. Here learn a simple starting point called the Calculus of Error. It can be understood with just intuition and some algebra.